Method for spatially distributing a population

ABSTRACT

A process for spatially distributing a population count within a geographically defined area can include the steps of logically correlating land usages apparent from a geographically defined area to geospatial features in the geographically defined area and allocating portions of the population count to regions of the geographically defined area having the land usages, according to the logical correlation. The process can also include weighing the logical correlation for determining the allocation of portions of the population count and storing the allocated portions within a searchable data store. The logically correlating step can include the step of logically correlating time-based land usages to geospatial features of the geographically defined area. The process can also include obtaining a population count for the geographically defined area, organizing the geographically defined area into a plurality of sectors, and verifying the allocated portions according to direct observation.

CROSS REFERENCE TO RELATED APPLICATION

[0001] Under 35 USC §119(e) this application claims the benefit of U.S.Provisional Application No. 60/428,616 entitled LandScan USA, filed onNov. 22, 2002, the entirety of which is now incorporated herein byreference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] The United States Government has rights in this inventionpursuant to Contract No. DE-AC05-00OR22725 between the United StatesDepartment of Energy and UT-Battelle, LLC.

FIELD OF THE INVENTION

[0003] The present invention relates to modeling census data, and moreparticularly, to modeling population distribution based geospatial data.

BACKGROUND OF THE INVENTION

[0004] Censuses provide a generally simple, and often used, form ofcollecting population data of a particular region. Since ancient Romantimes, controlling bodies utilized censuses for a variety of reasons,such as counting the population that lives in a particular region. Whilethe data collected in a simple census provides useful information, thelack of logical correlation of the population data to other factors ofthe region other than the boundaries of the region, limits the potentialextractable information from the population data. Censuses generallyreport the aggregate number of people residing in a region. Theseregions typically include entire towns, cities, states, and evencountries, each of which contain vast amounts of land area where nopopulation lives. Consequently, simple censuses of relatively largeregions provide little, if any, information regarding where individualmembers of a population generally live within that large region.

[0005] In many instances, people who constitute the population of aregion live in highly populated areas of that region, leaving otherareas of that region relatively unpopulated. Nevertheless, simplecensuses do not account for the reality of densely populated sub-regionsand sparsely populated sub-regions that are within a larger region.While a simple census provide some usefulness in determining theaggregate number of people residing in a region, some simple censusesinherently limit a systematic correlation of the location of apopulation throughout that region.

[0006] To account for the inherent limitations of a simple census, somemethods have been developed to systematically distribute members of aregion with a known population to sub-regions within a region. Pastmethods attempted to distribute members of a population based upongeographic features. Such a geographic correlation suffers from inherentlimitations due to the relatively large size of geographic features whencompared to the living area of an individual member of a population. Forexample, some methods correlate a higher incidence of population withina given region for areas with close proximity to such geographicalfeatures as impervious surfaces represented by roads, houses, and otherfeatures. Other geographic features that have been used to correlateincidence of population within a certain region include slope, landcover type, and intensity of nighttime lights. Nevertheless, suchindividual spatial relationships cannot reliably predict a populationdistribution for various reasons.

[0007] First, the relationship of the proximity of a relatively denselypopulated subregion to geographic features varies from region to region.For example, in a typical metropolis, there may be a positivecorrelation between the proximity of a densely populated sub-region to amajor road. For example, correlating a road to population density woulddistribute the population census for the shown region to be concentratedaround the road. While this may be an accurate description in someregions, in other regions an opposite correlation may exist such as adesert region having vast road networks with little population.Additionally, in a farmland region, there may be a negative correlationbetween the proximity of a populated sub-region to a major road.Further, in a suburban region, no correlation may exist between theproximity of a relatively densely populated sub-region to a major road.Therefore, while geographic features can provide a useful correlationfor distributing a population within a certain region, individualgeographic features alone do not provide a predictable and reliablerelation for distributing a population.

[0008] Additionally, while censuses usually limit data collection tolocations where members live, even correlations with geographic regionsdo not account for the reality of transient populations. In the mobileworld of today, transient populations exist on town, city, or even statelevel. Many people travel at least a few miles to work everyday, yetspend most of their nights in another location. Therefore, transientpopulations produce an affect on day time verse night time populations,an affect that is exacerbated with refinement of the region'sresolution.

[0009] A system that accounts for spatial and temporally refinedpopulation distribution data can provide a more accurate presentation ofthe population distribution for day or night. Such accurate informationprovides beneficial uses in a variety of applications incounter-terrorism, homeland security, consequence analysis,epidemiology, exposure analysis, urban sprawl detection, estimation ofpopulations affected by global sea level rise, and emergency planningand management for natural disasters, nuclear, biological, and chemicalaccidents. Terrorism, natural disasters, and technological accidents canstrike anywhere on earth, yet can have impacts on limited areas, such asneighborhoods, city blocks, and even buildings. Population distributionestimates on such a fine resolution help in planning for and respondingto such events.

SUMMARY OF INVENTION

[0010] The present invention relates to a process for distributing apopulation count within a geographically defined area. The process caninclude the step of logically correlating land usage to geospatialfeatures of the geographically defined area. Portions of a populationcount can be allocated to regions of the geographically defined areahaving associated land usages, according to the logical correlation. Theallocations subsequently can be stored within a searchable data store.Notably, the step of logically correlating land usages to geospatialfeatures of the geographically defined area can include logicallycorrelating time-based land usages to geospatial features of thegeographically defined area. Preferably, the logical correlation of landusages to geospatial features can be weighed for determining theallocation of portions of the population count.

[0011] In a preferred aspect of the invention, a population count can beobtained for a geographically defined area. Additionally, thegeographically defined area can be organized into a multiplicity ofsectors. Consequently, portions of the population count can be allocatedto at least one sector. In this regard, the sector can include a thirty(30) arc resolution and a three (3) arc second resolution. In any case,the allocation can optionally be verified according to directobservation. Finally, the process can be expressed as one of a computerprogram product.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] A fuller understanding of the present invention and the featuresand benefits thereof will be accomplished upon review of the followingdetailed description together with the accompanying drawings, in which:

[0013]FIG. 1 is a schematic diagram helpful in illustrating a processfor distributing a population within a geographically defined area inaccordance with the inventive arrangements.

[0014]FIG. 2 is a flow chart illustrating a process for distributing apopulation within a geographically defined area in accordance with theinventive arrangements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Geospatial features can include the geographic location andcharacteristics of natural and constructed features and boundaries ofthe earth and can include human bestowed characteristics, such as naminga geographically defined area. A non-exhaustive list of geospatialfeatures can include transportation networks, such as roads, waterways,railroads, subways, and the like, slope of the land surface, night timelights, and land cover, such as desert, arid grasslands, forests, water,wetlands, cultivated lands, man-made structures and the like. Ageographically defined area, by comparison, can include any land orwater mass having natural boundaries, political boundaries, orarbitrarily assigned boundaries.

[0016] Unlike geospatial features in a geographically defined area,“land usage” can refer to any form of interaction with geospatialfeatures within the geographically defined area. In this regard, landusage refers to qualitative and quantitative human interaction with thegeospatial features within the geographically defined area. Forinstance, land usage can describe general uses, such as industrial uses,residential uses, and agricultural uses, exhibited for a givengeographically defined area, irrespective of geospatial features. Landusage characterizations differentiate between buildings that appearrelatively similar to one another based upon satellite imagery.

[0017] For example, a land usage characterization can differentiatebetween a building used for a house, where typically one family resides,a building used as a factory, where typically no members reside duringnighttime hours but is highly populated during daytime hours, and aprison, where a vast number of residents reside within a confined space,even where the buildings appear identical in satellite imagery. Hence,while a building, no matter what the use of the building may be, can beviewed as relatively the same as other buildings, as seen in theprevious example, the actual land usage (i.e. the use of the building)is vital for accurately distributing a population count in ageographically defined area.

[0018]FIG. 1 is a schematic diagram helpful in illustrating a process100 for spatially distributing a population count within ageographically defined area in accordance with the inventivearrangements. For any given geographically defined area 110, thepopulation count 115 can be obtained through a variety of sources, suchas a government census, and through a variety of methods, such as adirect count, estimation, and other methods. The geographically definedarea 110 can have geospatial features 120, such as transportationnetworks, rivers, lakes, slope, elevation, rivers, buildings, forests,and the like. Additionally, within the geographically defined area 110,a variety of land usages 125 can exist, such as residential usages,agricultural usages and industrial usages.

[0019] The relationship between the land usages 125 and the geospatialfeatures 120 from the geographically defined area 110 can be used toform a logical correlation 130. The logical correlation 130characterizes the relationship between the land usages 125 and thegeospatial features 120. For a geographically defined area with avariety of geospatial features 120 and land usages 125, a multitude oflogical correlations 130 can exist. Each logical correlation 130 caninclude a type of land usage 125 that occurs proximate to and inconnection with one or more geospatial features 120. Therefore, thelogical correlation 130 can include multiple logical correlations 130for the same geospatial feature 120 having multiple land usages 125.

[0020] The logical correlation 130 of land usages 125 with geospatialfeatures 120 can be used for the allocation 140 of portions of thepopulation count to the geographically defined area 110. In operation,portions of the population count, such as individual members 145 of thepopulation and portions defined by a percentage of the population count,can be allocated 140 to regions of the geographically defined area 110as shown by the dotted lines pointing to particular regions of thegeographically defined area 110.

[0021] Optionally, the geographically defined region 110 can beorganized into a multitude of sectors 150 for simplifying the allocation140 of portions of the population count 115, as shown in FIG. 1. Here,the sectors 150 are shown as uniform squares to form a grid-basedorganization where all the sectors 150 are approximately the same shapeand the same size. Nevertheless, the invention is not limited to squareshaped sectors 150 as any appropriately shaped sector 150 can be used.Notably, the shape and size of the sectors can be dependent uponavailable resolution of satellite imagery, and therefore, the sectors150 can include an area of the earth's surface corresponding to a thirty(30) arc second resolution and a three (3) arc second resolution.

[0022] Turning to FIG. 2, the flow chart presents a more detailedillustration of the process described above, including variations anddifferent embodiments. The process 200 can begin at block 205 for anygeographically defined area having geospatial features and associatedland usages. In block 210, a population count can be obtained for ageographically defined area. Specifically, a population count forgeographically defined area can be obtained via a direct count,estimation, or from a source, such as an official government census.

[0023] In block 215, the geographically defined area can be organizedinto a plurality of sectors. In this regard, a grid-like organizationcan be provided in which all sectors have both the same shape and alsothe same size. Additionally, the grid like organization can providesectors that cover equal areas of the geographically defined region.

[0024] Alternatively, the shape of the sectors within a geographicallydefined area can also be tailored to reflect the geospatial featurespresent. The geographically defined area can also be organized as onesector mimicking the shape and size of the geographically defined area.Furthermore, the sectors can include sectors that can be dependent uponavailable satellite imagery, and therefore, the sectors can include anarea of the planet's surface corresponding to a thirty (30) arc secondresolution and even three (3) arc second resolution. Portions of thepopulation count of an entire geographically defined area can beallocated to sectors as will be discussed below.

[0025] In block 220, land usage can be logically correlated togeospatial features of the geographically defined area. Logicallycorrelating land usage to geospatial features can include determiningthe types of land usages that occur proximate to and in connection withgeospatial features. For instance, residential land usages can belogically correlated to buildings used for housing. In another example,agricultural land usages can be logically correlated to crop fields.Further, agricultural land usages can also be logically correlated tobuildings adjacent to or within the crop fields. Additionally, logicallycorrelating land usage to geospatial features can include characterizinga unique relationship between the land usage and the geospatial featuresfor the geographically defined area. Differences in economic, physical,political, and cultural factors necessitate a unique relationship for atleast geographically defined areas within different jurisdictionalboundaries.

[0026] In addition to logically correlating land usage to geospatialfeatures, block 225 can include logically correlating time-based landusage to geospatial features of the geographically defined area. Formany geospatial features, the land usage varies dependent upon the timeof day. Significantly, land usage can include transportation uses,indicating a high likelihood of members of population present duringpeak traffic times, residential uses, indicating a high likelihood ofmembers of a population present during night time hours, and industrialuses, indicating a high likelihood of members of a population countpresent during work hours but a low likelihood of members of apopulation count present during night time hours.

[0027] For example, many people commute to New York City on a dailybasis, but do not live within the bounds of New York City. Consequently,this daily flux of people can substantially affect the land usage ofgeospatial features within and proximate to New York City, such as theuse of transportation networks and the use of buildings as offices whichwill experience a greater use during business hours. In turn, thetime-based land usage can substantially affect the population dependingupon at what time the population is counted. In another example, thepopulation of a steep slope can vary greatly depending on the time thepopulation is counted, such as where a mountain for skiing can exhibit ahigh population during the day and the low to zero population during thenight. In such an instance, logically correlating time-based land usageto the geospatial feature can produce significant differences in thepopulation distribution.

[0028] In block 230, the logical correlation of land usage to geospatialfeatures of the geographically defined area can be weighed. For example,the land usage logically correlated with a road can include a weighingfor the distance of the land usage to the road. Similarly, other landusages logically correlated with geospatial features can be weighed;however, distance is not the only factor used and other factors such asintensity, area, and density can be used. The geospatial features andthe land usages can both be assigned values used to calculate alikelihood coefficient. The likelihood coefficient can be used toindicate that the logical correlation of particular land usages inproximity or in connection with geospatial features can increase ordecrease the likelihood of members of a population count being presentproximate to the geospatial feature. As an example in a givengeographically defined region, both land usages and geospatial featuresindicative of high populations can be assigned relatively high values,which are in turn used to calculate a resulting likelihood coefficientindicative of a high population count for that region of thegeographically defined area.

[0029] Additionally, the likelihood coefficient can uniquely weigh thelogical correlation of geospatial features and associated land usagespresent within the geographically defined area for at leastgeographically defined areas within a jurisdictional boundary. Such aunique weighing may be necessary for different geographic regionsdefined by jurisdictional boundaries due to political, social, andeconomic differences throughout the world. Thus, while a particularlogical correlation may be indicative of a high population distributionwithin certain parts of the world, the same logical correlation may beindicative of a low population distribution in other parts of the world.For example, while residents in the United States tend to reside at somedistance away from major highways, residents in other nations, such asIndia, tend to reside proximate to major highways. Thus, the,geographically defined area can influence the weighing of the logicalcorrelations.

[0030] Additionally, the weighing can be assigned a value. For example,the logical correlation of land usages with geospatial features withinthe United States that is indicative of low population, such as limitedland usage in portions of the Arizona desert, can be assigned arelatively low weighing value, such as a relatively low number. A lowweighing value can indicate a lack of population proximate to thegeospatial feature. In contrast, the logical correlation of land usageswith geospatial features within the United States that are indicative ofhigh population, such as densely developed residential living areas nearthe California coastline, can be assigned a relatively high weighingvalue, such as a relatively high number. A high weighing value canindicate a high population proximate to the geospatial feature. Itshould be noted the values discussed above are merely exemplary and thatgenerally, a wide range of values between low and high weighing valuescan accommodate the wide range of land usages and geospatial features ina geographically defined area and their influence on populationdistribution.

[0031] To reiterate, the weighing is not based solely on land usages orgeospatial features. Instead, the weighing is based on the logicalcorrelation of the land usage with the geospatial feature. Additionally,the weighing is not uniform and can differ for each geographicallydefined area. Therefore, while in some regions the logical correlation,of a steep slope used as a national park can indicate a lack of humanpopulation, in other regions the logical correlation of a steep slopeused as an agricultural region can indicate the likelihood of at least afew members of the population are present proximate to the steep slope.

[0032] Turning to block 235, portions of the population count can beallocated to the regions of the geographically defined area havingassociated land usages. Allocating portions of the population count canaffectively distribute members of the population proximate to geospatialfeatures with logically correlated land usages indicative of population.Generally, portions of the population can be allocated to regions sothat the aggregate of the portions will equal the complete populationcount for the geographically defined area. Also generally, greaterportions of the population count can be allocated to regions of thegeographically defined area having land usages, such as residentialuses, indicative of a relatively greater population in contrast toregions having land usages, such as agricultural uses, indicative ofrelatively less population.

[0033] Additionally, if the geographically defined area has beenorganized into a plurality of sectors in block 215, portions of thepopulation count can be allocated to a sector in block 240. Allocatingportions of the population count to sectors can be advantageous,particularly when the geographically defined regions are relativelylarge regions. Allocating portions of the population count to sectorscan also be advantageous, when a geographically defined region containsmultiple geospatial features covering small portions of thegeographically defined area while having logically correlated landusages that are strongly indicative of either high or low population.Additionally, in a situation where the geographically defined region hasbeen organized into a plurality of sectors based on the latitude andlongitude, such as 30 arc second sectors and even 3 arc second sectors,portions of the population count can be allocated on a sector by sectorbasis. Geographic allocation to relatively small geographic regionsselectively distributes portions of the population count and allows adetermination of the location of small portions of the population. Sucha determination can be helpful to predict portions of populationinvolved in events that affect only a small region.

[0034] In block 245, allocations can be stored within a searchable datastore. The data store can include any suitable form of memory includinga hard drive, ROM, RAM, Flash Memory, a cluster, a server, and the like.Storing the allocations within a searchable data store can allowcomputational analysis of the allocations and provides the informationfor future reference.

[0035] Turning to block 250, the allocations can be verified by directobservation. Verifying the allocations can include referencing sourceswith recorded information and can include methodical and systematic“door-to-door” counting of the population in particular regions.Verifying the allocations by direct observation serves as a check on theaccuracy of the process 200. Additionally, verifying by directobservation can serve a check on the precision of the resolution of thegeographically defined area or sector in which portions of thepopulation count is allocated. For example, a sector can be allocated aportion of the population; however, direct observation can indicate thatthe actual population distribution within the sector is substantiallyconcentrated to a smaller region within the geographically defined area.

[0036] In particular, allocations of low and zero population can beverified by direct observation to ensure accuracy. Some regions withingeographically defined areas contain geospatial features with logicallycorrelated land usages that are indicative of low to zero population.While this indication of low to zero population for some regions, suchas an area in the middle of a lake, is accurate, some regions stillinclude portions of the population. Such regions can include areas notused by mainstream society and are inadvertently described withinaccurate land usages, such as a park where, in many cities,substantial numbers of homeless people can be found. Therefore,verification by direct observation can ensure that the reality ofpopulation distribution is accounted for in the allocations. Process 200can end at block 260 or can begin again by returning to block 205.

[0037] The present invention can be realized in hardware, software, or acombination of hardware and software. Computer software which can beincluded as part of the present invention can be realized in acentralized fashion in one computer system, or in a distributed fashionwhere different elements are spread across several interconnectedcomputer systems. Any kind of computer system, or other apparatusadapted for carrying out the methods described herein, is suited.

[0038] A typical combination of hardware and software could be a generalpurpose embedded computer system with a computer program that, whenbeing loaded and executed, controls the computer system such that itcarries out the methods described herein. Computer program orapplication in the present context means any expression, in anylanguage, code or notation, of a set of instructions intended to cause asystem having an information processing capability to perform aparticular function either directly or after either or both of thefollowing a) conversion to another language, code or notation; b)reproduction in a different material form.

[0039] Significantly, this invention can be embodied in other specificforms without departing from the spirit or essential attributes thereof,and accordingly, reference should be had to the following claims, ratherthan to the foregoing specification, as indicating the scope of theinvention.

What is claimed is:
 1. A method for distributing a population countwithin a geographically defined area, the method comprising the stepsof: logically correlating land usages apparent from a geographicallydefined area to geospatial features in said geographically defined area;and allocating portions of the population count to regions of saidgeographically defined area having said land usages, according to saidlogical correlation.
 2. The method of claim 1, further comprising thestep of storing said allocated portions within a searchable data store.3. The method according to claim 1, wherein said logically correlatingstep comprises the step of logically correlating time-based land usagesto geospatial features of said geographically defined area.
 4. Themethod according to claim 1, further comprising the step of obtaining apopulation count for said geographically defined area.
 5. The methodaccording to claim 1, further comprising the step of organizing saidgeographically defined area into a plurality of sectors.
 6. The methodaccording to claim 5, wherein said allocating step comprises allocatingportions of the population count to at least one said sector.
 7. Themethod according to claim 5, wherein said sectors comprise a thirty (30)arc second resolution.
 8. The method according to claim 5, wherein saidsectors comprise a three (3) arc second resolution.
 9. A methodaccording to claim 1, further comprising the step of verifyingsaid-allocated portions according to direct observation.
 10. The methodaccording to claim 1, further including the step of weighing saidlogical correlation for determining said allocation of portions of thepopulation count.
 11. A machine readable storage having stored thereon acomputer program comprising a routine set of instructions for performingthe steps of: logically correlating land usages apparent from ageographically defined area to geospatial features in saidgeographically defined area; and allocating portions of the populationcount to regions of said geographically defined area having said landusages, according to said logical correlation.
 12. The machine readablestorage of claim 11, further comprising the steps of: storing saidallocated portions within a searchable data store; obtaining apopulation count for said geographically defined area; organizing saidgeographically defined area into a plurality of sectors; allocatingportions of the population count to at least one said sector; verifyingsaid allocated portions according to direct observation; and weighingsaid logical correlation for determining the allocation of portions ofthe population count.
 13. The machine readable storage of claim 11,wherein the logically correlating step comprises the step of: logicallycorrelating time-based land usages to geospatial features of saidgeographically defined area.
 14. The machine readable storage of claim12, wherein the organizing step comprises the step of: organizingsectors of at least one of thirty (30) arc second resolution-and three(3) arc second resolution.